About Me

I am a researcher at the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) focused on machine learning. I work with Prof. David Gifford in the Computational Genomics group, developing new interpretability methods for understanding deep neural networks and investigating novel approaches for designing therapeutics using ML.

Previously, I completed my undergrad and Masters at MIT, double majoring in computer science and mathematics. I also minored in economics. I graduated in June 2017 (undergrad) and June 2019 (MEng), advised by Prof. David Gifford.

My main interests broadly span machine learning and information, particularly as applied in computational biology and natural language processing. I am also intrigued by systems security and cryptography.

I have had the pleasure to work at Google Brain, Facebook, Bloomberg LP, KAYAK, and Leiden University.

I am originally from Long Island, New York. In my free time I enjoy sailing, hacking on various projects, and world traveling.


G. Liu, H. Zeng, J. Mueller, B. Carter, Z. Wang, J. Schilz, G. Horny, M. E. Birnbaum, S. Ewert, D. K. Gifford.  Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning. bioRxiv: 682880. 2019.
[bioRxiv] [pdf]

B. Carter, M. Bileschi, J. Smith, T. Sanderson, D. Bryant, D. Belanger, L. Colwell.  Critiquing Protein Family Classification Models Using Sufficient Input Subsets. ICML Workshop on Computational Biology. 2019.
[bioRxiv] [pdf] (Selected for oral presentation)

M. Bileschi, D. Belanger, D. Bryant, T. Sanderson, B. Carter, D. Sculley, M. DePristo, L. Colwell.  Using Deep Learning to Classify the Protein Universe. bioRxiv: 626507. 2019.
[bioRxiv] [pdf]

B. Carter, J. Mueller, S. Jain, D. Gifford.  What made you do this? Understanding black-box decisions with sufficient input subsets. Artificial Intelligence and Statistics (AISTATS). 2019.
[arXiv] [pdf]

B. Carter, J. Mueller, S. Jain, D. Gifford.  Local and Global Model Interpretability via Backward Selection and Clustering. NeurIPS Workshop on Interpretability and Robustness in Audio, Speech, and Language (IRASL). 2018.
[pdf] [slides] (Selected for oral presentation)

B. Carter, K. Leidal, D. Neal, Z. Neely.  Survey of Fully Verifiable Voting Cryptoschemes. 2016.

S.P. Epstein, K.B. Fernandez, B.M. Carter, S.A. Abdou, N. Gadaria, P. A. Asbell. Safety and Efficacy of Ganciclovir Ophthalmic Gel for Treatment of Adenovirus Keratoconjunctivitis Utilizing Cell Culture and Animal Models. Invest. Ophthalmol. Vis. Sci. 2012;53(14):6203.


Click on any of the projects below to learn more. You can also take a look at some of the contributions I have made on GitHub.

Twitter NLP Twitter NLP Follower Prediction
ICU Patient Predictions ICU Patient Predictions
Academics for the Future of Science Academics for the Future of Science
Ploegh Lab Website Ploegh Lab Website
StudentsThink StudentsThink


My email is bcarter [at] mit [dot] edu. Feel free to also connect with me on LinkedIn.

I look forward to getting in touch!